Optimal Control of Tunable PMD Compensator Using Random Step Size Hill-Climbing Method

2008 ◽  
Author(s):  
Ken Tanizawa ◽  
Akira Hirose
Keyword(s):  
Optimal Control ◽  
Hill Climbing ◽  
Step Size ◽  
Random Step

Low-Complexity Time-Domain DBP Based on Random Step-Size and Partitioned Quantization

2018 ◽  
Vol 36 (14) ◽  
pp. 2888-2895 ◽  
Author(s):  
Celestino S. Martins ◽  
Luca Bertignono ◽  
Antonino Nespola ◽  
Andrea Carena ◽  
Fernando P. Guiomar ◽  
...  
Keyword(s):  
Time Domain ◽  
Low Complexity ◽  
Step Size ◽  
Random Step

scholarly journals Sequential Decision-Making in Ants and Implications to the Evidence Accumulation Decision Model

2021 ◽  
Vol 7 ◽  
Author(s):  
Oran Ayalon ◽  
Yigal Sternklar ◽  
Ehud Fonio ◽  
Amos Korman ◽  
Nir S. Gov ◽  
...  
Keyword(s):  
Decision Making ◽  
Variable Step Size ◽  
Sequential Decision ◽  
Sequential Decisions ◽  
Step Size ◽  
Evidence Accumulation ◽  
Biased Random Walk ◽  
Paratrechina Longicornis ◽  
Cooperative Transport ◽  
Random Step

Cooperative transport of large food loads by Paratrechina longicornis ants demands repeated decision-making. Inspired by the Evidence Accumulation (EA) model classically used to describe decision-making in the brain, we conducted a binary choice experiment where carrying ants rely on social information to choose between two paths. We found that the carried load performs a biased random walk that continuously alternates between the two options. We show that this motion constitutes a physical realization of the abstract EA model and exhibits an emergent version of the psychophysical Weber’s law. In contrast to the EA model, we found that the load’s random step size is not fixed but, rather, varies with both evidence and circumstances. Using theoretical modeling we show that variable step size expands the scope of the EA model from isolated to sequential decisions. We hypothesize that this phenomenon may also be relevant in neuronal circuits that perform sequential decisions.

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